Acute lymphoblastic leukemia (ALL) is a disease that exhibits heterogeneity in clinical outcome. Some of this heterogeneity is reflected by the presence of distinct nonrandom chromosomal translocations, which are associated with distinct clinical outcomes. Many of these chromosomal translocations result in novel fused genes. We hypothesize that these fused genes induce aberrant gene expression patterns and that this is the mechanism by which these translocations contribute to the development of leukemia and, consequently, affect prognosis. However, not all of the heterogeneity in clinical outcome can be explained by translocations. We hypothesize that other specific heterogeneous features of ALL, such as variable response to chemotherapeutic agents, are also reflected in distinct patterns of gene expression. The broad goals of the project described in this proposal are to use measurements of the expression levels of a large number of genes in ALL to gain a better understanding of specific features of ALL. In order to make such measurements, we will exploit recently developed high-density cDNA microarray technology. The specific aims of this project are: (1) to identify distinct patterns of gene expression that are associated with particular chromosomal t r anslocations, (2) to identify distinct patterns of gene expression associated with sensitivity or resistance to chemotherapy, (3) to use gene expression measurements to identify important diagnostic and prognostic subtypes of ALL, that have not previously been recognized, and (4) to develop testable models for the gene transcription pathways involved in ALL. Through this work, we expect to make a number of important contributions to the understanding of ALL. First, we will gain a deeper understanding of the molecular mechanisms responsible for the development of ALL. Second, we will gain insight into the mechanisms of chemotherapy resistance in ALL and, thus, facilitate the development of methods to overcome this resistance. Third, through the identification of previously unrecognized subtypes of ALL, we expect to facilitate the development of novel, clinically useful markers to guide treatment in this disease. Fourth, by exploiting novel modeling techniques, we expect to gain a deeper understanding of the gene transcription networks that are important in ALL.